High-pressure BaCN$_2$ phases explored by genetic algorithm
Peng Song, Mari Kawaguchi, Yuji Masubuchi, Kenji Oqmhula, Kousuke, Nakano, Ryo Maezono, Kenta Hongo

TL;DR
This study uses genetic algorithms and ab initio calculations to predict high-pressure phases of BaCN$_2$, revealing a new polymerized structure with potential stability above 42 GPa.
Contribution
It introduces a novel computational approach combining genetic algorithms with ab initio methods to predict high-pressure crystal structures of BaCN$_2$.
Findings
Reproduced known crystal structures at lower pressures.
Predicted a new polymerized phase with Ima2 symmetry above 42 GPa.
Identified covalent bonding between CN$_2$ units stabilizing the high-pressure phase.
Abstract
Polymers containing nitrogen have attracted much attention in connection with their application to high energy density materials (HEDMs), in which energy is inherent in the triple bond. It is an interesting question whether such polymerized phases appear in the high-pressure phase of metal carbodiimide MCN, of which synthesis have been reported in recent years, but few studies have investigated the crystal structure at high pressure. We have adopted a structure search based on the genetic algorithm coupled with ab initio electronic structure calculations to investigate possible crystal structures that may appear in the high-pressure phase of BaCN. The structure search successfully reproduced the previously reported crystal structures in the lower pressure range. With confirmed reliability of its predictive ability, the genetic search further predicts a polymerized phase with…
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Taxonomy
TopicsBoron and Carbon Nanomaterials Research · Metal and Thin Film Mechanics · Machine Learning in Materials Science
